Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM

VR-PMS: a new approach for performance measurement and management of industrial systems

A new performance measurement and management framework based on value and risk is proposed. The proposed framework is applied to the modelling and evaluation of the a priori performance evaluation of manufacturing processes and to deciding on their alternatives. For this reason, it consistently integrates concepts relevant to objectives, activity, and risk in a single framework comprising a conceptual value/risk model, and it conceptualises the idea of value- and risk based performance management in a process context. In addition, a methodological framework is developed to provide guidelines for the decision-makers or performance evaluators of the processes. To facilitate the performance measurement and management process, this latter framework is organized in four phases: context establishment, performance modelling, performance assessment, and decision-making. Each phase of the framework is then instrumented with state of-the-art quantitative analysis tools and methods. For process design and evaluation, the deliverable of the value- and risk-based performance measurement and management system (VR-PMS) is a set of ranked solutions (i.e. alternative business processes) evaluated against the developed value and risk indicators. The proposed VR-PMS is illustrated with a case study from discrete parts manufacturing but is indeed applicable to a wide range of processes or systems

Artifıcial Intelligence Integration of Large Logistics Companies

In the digital world with a focus on customer experience, the journey that begins in the virtual realm is completed with a positive impact when it turns into a physical experience, thanks to successful operations in logistics processes. The purpose of this article is to systematically examine the integration of artificial intelligence into processes in logistics management within the supply chain, thereby providing a framework for businesses in the industry. The main motivation for the methodology developed through the process-technology alignment in logistics management is derived from existing research in the literature. The seven areas in logistics management identified by Mentzer et al. (2008) serve as the theoretical guiding principles and foundation of this study. Building upon this foundation, the research question of how companies can achieve the integration of artificial intelligence in logistics management domains has been answered